Baseball and other nonsense

Tag Archives: Baseball

A few months ago, a brilliant xkcd comic inspired a challenge within the science world: explain a complex scientific topic using only the 1,000 most common words in the English language. My first thought was, of course, “Why not try this with baseball?”. Using theUp-Goer Five Editor, I attempted to tell the story of last year’s AL MVP race. Here is the result (originally published on Beyond the Box Score):

Every year, people that write about hit-ball decide who they think was the best hit-ball player. Sometimes it’s easy to decide because there is one player that is better than every other player and everyone knows it. Last year was not one of those times. There was one player that was better than every other player, but many people were confused about this. Many people thought that a different player was better than every other player, even though he was not.

People thought that this player was better than every other player because he beat every other player in Three Numbers: times getting a hit for every time he had a chance to get a hit, times hitting the ball over the wall, and times getting another player to touch the last bag. For many years, these Three Numbers have been very important to people that watch hit-ball, so people really like when someone leads everyone in all three…

Yesterday, I put up a poll about some thought experiments regarding performance enhancing drugs and the Hall of Fame. The poll didn’t really function as I had hoped (or have a high turnout), but the purpose was to figure out why people are really for or against voting PED users into the Hall of Fame. I’m curious about what factors and reasons are pulling the threads, and where the limits are for those factors, for both camps.

One such factor is integrity versus performance. Are PEDs bad because they give those who take them an unfair advantage? Or are they bad because it is unethical to take them, and therefore those who do take them violate the “character clause”?

If the former, that PEDs are bad because they affect performance, we must first know the extent to which they affect performance before we can decide whether to vote for a player suspected of PEDs. This is a big task, and one that can’t be accomplished right now given our current knowledge. However, it really doesn’t matter, because it seems clear that most writers (and fans) are against players that took PEDs not because of their effect on performance, but because of their effect on integrity.

Even within the integrity complaint, there are multiple reasons why one might be against voting in the PED guys. One is that PEDs are an attempt to cheat the game of baseball and get an unfair advantage over the rest of the field, which is a “crime” that is unforgivable with regards to the Hall of Fame.

The other option is that taking PEDs is cheating, which is unethical, and players who are sufficiently unethical should not be allowed in the Hall of Fame. This seems a little silly, but one of my examples from the poll might help. Say a player on the ballot, who retired many years ago, was recently convicted of first-degree murder that he committed before he retired. This murder obviously had nothing to do with baseball, but it was horrifically unethical nonetheless. Would you vote him into the Hall of Fame?

If you answer no to that question, then you are admitting that a player’s character, even outside the game of baseball, does in fact matter. Which means that the ethics of taking PEDs is a matter of degree, not kind. Of course, if you claim that taking PEDs is too unethical to be voted into the Hall of Fame, you must either say that Hall of Famers who have done more unethical things should not have been voted in in the first place, or that taking PEDs is a worse offense than any act a current Hall of Famer has committed.

Finally, we must think about certainty. Even if we take for granted that PEDs aid performance significantly and/or those that take PEDs should not be allowed in the Hall of Fame, what degree of certainty that a player in fact took PEDs must we have to make a decision? 95% certainty? 75%? 50%?

Do other factors matter in this case? What if the player is a shoe-in for Hall of Fame otherwise? Do we need to be more certain in that case? If the player is a borderline Hall of Famer, is any shadow of doubt as to his cleanliness enough to not vote for him? What about the potency of the drug? Is less certainty necessary for steroids than for HGH?

Obviously, I asked more questions than I answered today. But that’s the point. The issue of PEDs is not a simple one, not even close. Anyone on either side that tries to claim that is very mistaken. Every assumption, every reason, on one side or the other, raises a plethora of issues and questions, most if not all of which must be answered before one can attempt to justify their decision.

Share your thoughts about any or all of these issues and questions below. And please, don’t resort to hyperbole or oversimplifications, as so many do in these discussions. There are no easy answers, and if we all admit that to ourselves, we can move discussion forward rather than yell over each other’s heads.

Not much to say here. Simply trying to get a sense of everyone’s motivations for their beliefs regarding the Hall of Fame and performance enhancing drugs. I can think of many more interesting examples, but 10 should suffice for now. Let me know if you have any questions.

1) We know that Drug A 100% guarantees that a player will play at Hall of Fame caliber. Player A, who is a shoe-in for the Hall of Fame based on performance alone, tests positive for Drug A on a foolproof test.

2) Player B, who is a shoe-in for the Hall of Fame based on performance alone, tests positive for Drug A on a test that is correct 95% of the time.

3) Player C, who is a shoe-in for the Hall of Fame based on performance alone, tests positive for Drug A on a test that is correct 75% of the time.

4) Player D, who is a shoe-in for the Hall of Fame based on performance alone, tests positive for Drug A on a test that is correct 50% of the time.

5) We know that Drug B is guaranteed to improve performance by 10%. Player E, who is 10% better than the Hall of Fame threshold (pretend such a thing exists), tests positive for Drug B on a foolproof test.

6) Player F, who is 50% better than the Hall of Fame threshold, tests positive for Drug B on a foolproof test.

7) Player G, who is a shoe-in for the Hall of Fame based on performance alone, was convicted – after he retired – of a series of murders he committed while he played.

8) Player H, who is just above the Hall of Fame threshold, was convicted – after he retired – of a series of murders he committed while he played.

9) Player I, who is a shoe-in for the Hall of Fame based on performance alone, received multiple DUIs while playing.

10) Player J, who is just above the Hall of Fame threshold, received multiple DUIs while playing.

What would baseball be like without stats? I don’t mean advanced stats – I mean ALL stats. All of them. No batting average. No RBI. No HR. No WAR. Nada. What if we watched baseball the same way that we do now, but no one kept track of results, at least outside of their own minds? What if broadcasts didn’t flash stat lines on the bottom of the screen, announcers didn’t mention them in their commentary, and they didn’t appear in newspapers?

I wonder how well we would be able to judge the value of a player in that case. It might be a little easier with pitchers, since their performance comes in bunches. We can tell when a pitcher is doing badly pretty easily, because we can see the batters getting hits and coming around to score all within a few minutes. But it would still be difficult to compare pitchers over an entire season, wouldn’t it? Sure, we can tell when a pitcher has a bad game, but what if one pitcher had 10 bad games in a season and another had 8 bad games in a season? Would we be able to remember that? Would we be able to remember how bad those games were? Probably not.

It’s even harder for hitters. They only come up every nine plate appearances, not to mention the break for the other team to bat. And they have 600+ plate appearances in a season. If we didn’t keep track of stats, we would have to remember all of these plate appearances. Well, maybe not all of them, but at least a vast majority if we wanted to be confident about how a player performed.

As an analogy, imagine watching someone roll an uneven die (as in, each number does not come up at the same rate) 600 times and then guessing how many times a 1 or 2 came up. But you only see about 4 or 5 rolls a day, and they come every half hour or so. How well do you think you would be able to estimate the percentage that a 1 or 2 came up? Could you get within 2%? 5%?

Say you estimated that a 1 or a 2 came up 32% of the time. In actuality, they came up 28% of the time. You would probably be proud at getting close, especially since you know it’s not an even die. 32% sure doesn’t seem too far away from 28%. Well, you just said that a .280 hitter was a .320 hitter. When given Neil Walker, you said Ryan Braun. And that’s when you were within 4% of being correct. What if you were off by 8%? That’s certainly possible, given the length of time in which you were given to remember these rolls. Then you would basically be mistaking the league leader in batting average for Mark Teixeira. You would have absolutely no authority to make a claim about the rolls or players.

The above example was the equivalent of only paying attention to one batter and only caring about hits. When you add in the rest of the players and the multitude of possible outcomes, how could you possibly remember enough to judge one player better than another without stats? Sure, you could probably tell the difference between the pitcher and Miguel Cabrera, but could you tell the difference between Mike Trout and Miguel Cabrera? Maybe you’d be able to tell that Cabrera hit more home runs and you could definitely conclude that Trout stole more bases. You’d likely be able to tell that Trout was a better fielder, but it would be virtually impossible to tell who was a better hitter. After all, if you were off by, say, 2% on both players’ batting average in opposite directions, suddenly there’s a 40 point difference! You’d be saying that Cabrera hit .350 versus Trout’s .306, or Trout hitting .346 to Cabrera’s .310. If either one of those scenarios were true, the MVP race would probably be unanimous.

This is not my way of saying that stats are all that matters. This is my way of saying that stats are, at the very least, absolutely necessary if we want to measure value. The season is just too long to use only your eyes and memory. There’s no way you could possibly distinguish between a .330 hitter and a .320 hitter, or someone with 45 home runs and someone with 40 home runs, or someone with a 3.20 ERA and someone with a 2.80 ERA. Yet all those distinctions are important. If we want to measure value, we have to use stats. And we all do use stats every day during the season. We see a player’s batting average and home run total, at the very least, every game. That influences what we think of a player. I’m not saying that watching Miguel Cabrera or Mike Trout wouldn’t be impressive to watch without stats, but that we would have no way of knowing just how impressive they are.

Assuming .161 runs saved by turning a ball into a strike (see here), Molina would need to turn a ball into a strike about once every 36 or 37 pitches to save 50 runs in a season (with 710 innings caught).

Two questions:

1) Is my math right?
2) Is that conceivable? Seems to me that it could be.

I’m reposting this from Big Leagues Monthly: Daily Edition. You can find the original here.

I have never been a Giants fan, despite living most of my life in the Bay Area. I was a Yankees kid through and through, and for the most part, I’ve never really been able to emotionally invest in another team, baseball or otherwise. Yet for the past few weeks, I’ve found myself helplessly caught up in the Giants magic. Though my allegiances remain with the Yankees, the Giants have taken hold of my emotional attention.

This probably isn’t an alien notion for many of you. As a Yankees fan, I’ve been privileged to see my team make the playoffs almost every year, so I normally don’t have the time or emotional capacity to spread my roots to another team in the playoffs. But I would imagine that fans of teams with less financial ability have an easier time finding a team to root for once theirs is eliminated.

*I’m not sure what has made this year different in that regard. It probably has to do with a combination of the Yankees losing in such a dreadful manner, the Giants’ social media (see, Twitter) presence, and my increasingly sophisticated interest in baseball.

This is all well and good. But what happens when the playoffs end, when the team is either eliminated or crowned champion? Well, at this point, there are three possibilities for those who had a rooting interest in said team:

1. Cease to root for the team next season, regardless of their success.

2. Maintain interest in the team as long as they are successful, but stop caring when they do poorly.

3. Become a loyal fan of the team, continuing to follow and cheer for them through thick and thin.

Now, some of these options are obviously more favorable than others. Yet in each of them, one could conceivably be labeled as a “bandwagon fan”. After all, regardless of the option, this fan “jumped on the bandwagon” of a team, primarily because they were winning.

Let’s start with option 1. This is probably the group that I’m in with regard to the Giants. I’m having a ton of fun cheering for them right now, but I know it won’t last. Sure, I might be slightly more interested in their goings-on next season, but I won’t have an active rooting interest in the team like I do right now.

Is that bad? Am I a bandwagon fan for caring about the Giants right now just because they’re winning (in a spectacular fashion I must add)? Well, possibly yes to the second question, but I don’t think that’s a bad thing. I’m a baseball fan, after all. Though my love for baseball originates from, and is anchored by, my love for the Yankees, the former is absolutely not limited to the latter. I find joy not only in watching baseball as an observer, but in participating (in a broad sense of the term) as an active rooting interest. Sports are more fun when you care about who wins. Why criticize those who care about a team other than their primary one?

Option 2 is more cut and dry. These are people commonly known as “fair-weather fans”. They root for a team when they do well, and ignore them when they do badly. There’s no true commitment – all the “fan” wants to do is see their team win, and nothing else. Most committed fans hate this group, because they make the rest of the fans look bad by having what they perceive to be a fake interest in the team.

At this point there are a few points I want to raise. First of all, I think that fair-weather fans take too much flak. After all, they are presumably supporting the team by buying merchandise, going to games, watching games on TV, etc. In doing so, they help the team to raise revenue and continue being successful. Yes, it’s unquestionably annoying when these fans pretend to be “true” fans, espousing their unwavering commitment to the team, only to stop following them when they stop winning, but there’s nothing wrong with getting excited when your team is successful and it’s understandable to lose interest when they aren’t.

This leads to my second question – what’s the relevant difference between #1 and #2 above? Aren’t fans of the second variety just fans of the first variety who continue to cheer for the team until they start losing? Why is it better to just stop caring when the playoffs are over than to stop caring in a year or two when the team is unsuccessful?

Here’s my answer, though I won’t profess to be confident in its veracity: when you make a conscious decision to be a fan of a team, you are making a commitment to that team that goes beyond simply cheering for them for one season or just when they win. In professing yourself as a fan, you are committing to a certain level of involvement and awareness of what happens to the team and you are promising that you will not cease to be a fan just because the team is unsuccessful. You aren’t saying that you’ll always be a fan of the team, or that you’ll cheer for them no matter what, but that your loyalty to them is independent of their success.

“But Matt, that doesn’t resolve the issue! What makes the second option so much more than the first?”

Well, dear reader, here’s the catch – people who are in the first camp, and to some extent the second, don’t necessarily call themselves fans. I certainly don’t profess to be a fan of the Giants. This is because rooting for a team and being a fan of a team are very different acts. Anyone can root for any team at any time for any reason. There is no criteria for rooting, no reasonable criticism to make against people who root for a particular team at any given time.

But being a fan is different. When you call yourself a fan of a team, you are held to a certain standard of loyalty and commitment. It’s fine to be a bandwagoner, to cheer for a team just because they are winning or doing well in the playoffs. But if you decide to be a fan of the team, your commitment must thereafter be grounded not on the original reason for your interest in them (winning), but on a love for the team itself.

I’ll leave you with one final thought, since I never really hit on that third option above. Even if my above conclusion works, it is often impossible to tell the difference between self-described fans of the second and third variety. That is, when a team is successful, there’s no way to tell which fans are true, committed fans, and which are bandwagon/fair-weather fans. You know that anyone who roots for and follows the Pirates right now is probably a true fan, but with teams like the Yankees, there’s no way of separating the wheat from the chaff.

The solution to this dilemma for many is to assume that all people who began following the team when they started winning are bandwagon fans. But all this does is create a barrier among fans; by believing yourself to be a superior breed of fan just because you followed the team when they were bad, you prevent growth and comradery within the fan base, which can only harm the team.

So instead of assuming that all new fans are inauthentic and fake, why don’t we assume the opposite? Why don’t we assume that, despite coming to the fandom because of the team’s success, these fans are here to stay? Sure, many will prove themselves to be inauthentic once the team’s success runs out, but let’s wait until that happens before we assume anything. Rooting for teams that are successful is great and becoming true fans of those teams is even better!

We should applaud and support people who root for, and become fans of, our team. At the same time, if you do find yourself rooting for a new team, or getting caught up in the magic of your home team for the first time, enjoy it! But think carefully before you call yourself a fan of that team. True fans stick with their team day in and day out. They feel joy with every win and pain with every loss, but remain true through the ups and downs. It can be tiring, heartbreaking, and downright terrible, but in the end, it’s worth every second.

This post is about the AL MVP race. I’m sure you’re tired of reading about this, but I’m going to write about it anyway. I’ll try to make it quick.

Take a look at this table:

1B

2B

3B

HR

SB2

CS2

SB3

CS3

Mike Trout

116

26

8

30

43

3

6

1

Miguel Cabrera

121

40

0

44

3

1

1

0

SB2 are stolen bases of second, and SB3 are stolen bases of third.

That leads to these lines:

AVG

OBP

SLG

OPS

Mike Trout

0.324

0.397

0.561

0.958

Miguel Cabrera

0.331

0.394

0.608

1.002

But stolen bases of second are really just turning singles into doubles, right? I know there are differences, but let’s just assume that that’s what they are. In the same way, stealing third turns a double into a triple. So using those stolen base totals, we’re going to turn 43 of Mike Trout’s singles into doubles (and 3 of his singles into outs), as well as turn 6 of his doubles into triples (and 1 of his doubles into an out). We’ll do the same with Miguel Cabrera.

That leaves of with this:

AVG

OBP

SLG

OPS

Mike Trout

0.327

0.400

0.662

1.062

Miguel Cabrera

0.331

0.394

0.616

1.010

As you can see, Trout now has an OPS that is well higher than Cabrera’s OPS. Interesting, right?

Of course, this was not a very great method of taking stolen bases into account. A single and a stolen base is obviously not the same as a double because of the whole driving in runs thing. But I’d say it’s probably pretty close in value. wOBA (the metric that WAR uses) does a much better job of properly valuing stolen bases, but for those who prefer traditional stats, I thought this might appeal to you. Even if we drop down the value of stolen bases a bit to account for the slight difference, Trout and Cabrera end up with a very similar OPS. And then, of course, we should take defense into account, which Trout probably wins by a large margin.

What do you think? Is this at all convincing for the Cabrera supporters among you?

This post doesn’t require much explanation. Here are 5 fun GIFs of things that the Yankees did this week. Enjoy.

5. This isn’t the flashiest of plays, but it was a huge double play in a must win game for the Yankees. Martin has really stepped it up in September, offensively and defensively.

4. This one is just funny. I always used to look at my mitt whenever I would miss a catch in Little League, but I didn’t think the ball could actually go straight through my mitt. Of course kids didn’t throw 90+ mph in Little League.

3. I love this. Not sure Nick Swisher actually had to do that little jump throw, but it definitely made the play more interesting.

2. I’m cheating on this one, as it was technically 8 days ago, but come on. I could watch this all day.

1. This has got to be one of the best catches of the year. He didn’t dive or climb the fence or throw someone out at home, but there are very few players in baseball that could make this catch. Even at 38, Ichiro Suzuki is still a defensive wizard.

So there you have it. My top 5 Yankees plays of the week. I’m sure I missed some here and there (and some were un-gif-able), but I think these are all pretty entertaining, if I may say so myself. Hope you enjoyed.

Last week, I posted an idea about how to evaluate pitching. It would help if you read that first, but not necessary. My question isn’t really about the idea, but how to implement it.

Basically, I want to give fans an opportunity to rate balls in play as they watch games. However, in order for my idea to work, this needs to happen almost immediately after the play is over, or people will forget what happened.

Unfortunately, the game data just doesn’t update fast enough for fans to be able to do this. They would have to wait at least 10-15 seconds, and probably much longer, before they could choose which play to rate. By that time, memories of the play will have been muddled, and the data would be much less valuable.

What this means is that I will need to collect these responses from fans somehow, BEFORE the information about that play (pitcher, batter, inning, base/out state, etc.) is released online. Here are the solutions I can think of so far:

Have fans input all the contextual information themselves.

Have fans input just batter and pitcher and get the rest based on time that the response was submitted.

Have fans input inning and base/out state (and maybe score), and fill in the other info later.

Accept that there will be a delay and hope that fans remember the play well enough to answer accurately.

I think option 3 is by far the best so far, but I’m sure there are better options out there. That’s where I need your help. How should I do this with as much accuracy and as little work on my part and the part of fans as possible?

In this post, I will, yet again, not only bring together two nerdy activities, fantasy baseball and programming, but I will also write about the amalgamation of said activities in moderate detail, which is enough detail to make the vast majority of you bored and somewhat uncomfortable. Pumped? Me too, my friends, me too.

Last week, I talked about my experimenting with creating a z-score spreadsheet for fantasy baseball projections. The other project that I’ve been working on is, or at least will be, a little more theoretical. Well, I’m not sure theoretical is the right word for it. Let me explain.

The question: What makes a fantasy baseball team win? More specifically, in head-to-head leagues, do certain types of teams, or teams with certain types of players, perform better than others, even if their overall stats are similar?

The end goal: Simulate fantasy baseball seasons using various types of team builds.

The process: Ha. As if I actually know how to go about doing this. But I’ll try, because I have a week until I start my new job and nothing to do. Instead of telling you what I’ll do, I’ll show you what I’ve done so far, then my plan – if I have one – for the future of the program.

Well it’s pretty simple. “createTeams” creates two dictionaries: the first pairs the number of the team with a list of values that represent the probability of that team winning a particular category against an average opponent in one week. The second dictionary pairs the team number with their win-loss record. (As I write this, I’m realizing that there is no point in these being dictionaries if the keys are just numbers, since lists are ordered anyway.)

The second function, “simulateWeek”, is my attempt to – you guessed it! – simulate a week in a fantasy baseball head-to-head league. Basically, I iterate through teamDict and create matchups so that each team plays once (probably only works with an even number of teams).

Determining the probability of one team beating another in a category given two probabilities was tough, but after some research I came across Bill James’ “log5” formula, which calculates a team’s chance of beating another team given their respective winning percentages. I used this formula to calculate the chance of one team beating the other in a category, then used numpy’s geometric distribution sample function to generate one trial based on the log5 probability. If the trial “succeeds”, team1 wins; otherwise, team2 wins, and their records are consequently updated.

So that’s what my program does so far. The good news is that it works! In my main function I called the simulateWeek function 20 times to simulate a season, with 12 teams in the league and 10 categories. It returned the probabilities of each team winning the categories as well as their final records.

The bad news is that it’s pretty much useless at this point. I’ve created a nice little simulation of a season using random probabilities, but remember that my goal is to simulate with teams of differing structures and types of players. For example, would a team with a bunch of good relievers and only a few good starters be better than a team with a ton of middle-of-the-pack starters and only a few mediocre closers? What about a team that punts batting average in exchange for more home runs and RBIs?

To do that kind of simulation, I need to do more than provide random probabilities for the categories. I probably need to use some sort of projection system so that for every week simulation, the program creates totals for the categories based on the projections and random variation. This seems doable at first, but like all programmable ideas, I’m sure it will take a lot more time and effort than one would initially think.

I’ll get working on this and sometime in the future, write up another post updated you all on my progress. Until then, let me know if you have any suggestions, comments, questions, etc. I’m writing up these posts partly because I think writing my thoughts down will help me think about these projects, but also because a lot of you are much better at this than I am, and I want help. So help me.